{"id":"W2074599695","doi":"10.1016/j.jmgm.2010.08.003","title":"Development of accurate binding affinity predictions of novel renin inhibitors through molecular docking studies","year":2010,"lang":"en","type":"article","venue":"Journal of Molecular Graphics and Modelling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Docking (animal); Chemistry; Computational biology; Computer science; Biology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005729486,0.0006675593,0.0009011715,0.0005445625,0.0002834766,0.0007482992,0.0008274663,0.0004626459,0.001471728],"category_scores_gemma":[0.001342884,0.0005874335,0.000397768,0.0004193358,0.0001657626,0.0005580902,0.0002707152,0.0006036754,0.0006290919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005275825,"about_ca_system_score_gemma":0.0006606653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993683,"about_ca_topic_score_gemma":0.002888017,"domain_scores_codex":[0.9997813,0.00005522158,0.0000142716,0.0000184985,0.0001061091,0.00002455238],"domain_scores_gemma":[0.9996133,0.000187592,0.00004328872,0.00003787968,0.0001019108,0.00001612755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003329483,0.0001463919,0.001706181,0.0001237586,0.0000662233,0.0002056838,0.00003076432,0.8709566,0.05534162,0.003578474,0.001139731,0.06637172],"study_design_scores_gemma":[0.00003585317,0.00005329869,0.0002325978,0.000002545122,0.00001650708,0.00002768405,0.000007702437,0.9788678,0.0198266,0.0005456583,0.0003756703,0.000008080357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4590829,0.001575986,0.5265836,0.0003185402,0.00004688303,0.0002053265,0.0007704693,0.003316411,0.008099927],"genre_scores_gemma":[0.9344825,0.0005838905,0.06308009,0.00003439572,0.000008733827,0.00008573786,0.0004187456,0.0000770372,0.00122893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002993683,"threshold_uncertainty_score":0.005952477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08076170836432192,"score_gpt":0.3388324780166417,"score_spread":0.2580707696523198,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}